Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage.

van Zoest V, Lindberg K, Varotsis G, Osei FB, Fall T

Spat Spatiotemporal Epidemiol 48 (-) 100636 [2024-02-00; online 2024-01-17]

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

Category: Health

Category: Vaccines

Funder: Hjärt-Lungfonden

Funder: VR

Funder: Vinnova

Type: Journal article

PubMed 38355257

DOI 10.1016/j.sste.2024.100636

Crossref 10.1016/j.sste.2024.100636

pii: S1877-5845(24)00003-0

Publications 9.5.0